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Abstract #3614

Model-augmented deep learning for VFA-T1 mapping

Lea Bogensperger1,2, Oliver Maier1, and Rudolf Stollberger1,3
1Institute of Medical Engineering, Graz University of Technology, Graz, Austria, 2Institute for Computer Graphics and Vision, Graz University of Technology, Graz, Austria, 3Biotechmed, Graz, Austria

A deep learning approach is proposed to estimate M0 and T1 maps from undersampled variable flip angle (VFA) data to explore the potential of this method for acceleration and rapid reconstruction even without parallel imaging. A U-Net was implemented with a model consistency term containing the signal equation to ensure the physical validity and to include prior knowledge of B1+. Training is performed on numerical brain phantoms and by means of transfer-learning on retrospectively undersampled in-vivo data. Qualitative and quantitative results show the acceleration potential for both numerical and in-vivo data for acceleration factors R=1.89, 3.43, and 5.84.

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